Search results for: iterated local search

Authors: Alireza Osareh, Bita Shadgar

Abstract:

Segmenting the lungs in medical images is a challenging and important task for many applications. In particular, automatic segmentation of lung cavities from multiple magnetic resonance (MR) images is very useful for oncological applications such as radiotherapy treatment planning. However, distinguishing of the lung areas is not trivial due to largely changing lung shapes, low contrast and poorly defined boundaries. In this paper, we address lung segmentation problem from pulmonary magnetic resonance images and propose an automated method based on a robust regionaided geometric snake with a modified diffused region force into the standard geometric model definition. The extra region force gives the snake a global complementary view of the lung boundary information within the image which along with the local gradient flow, helps detect fuzzy boundaries. The proposed method has been successful in segmenting the lungs in every slice of 30 magnetic resonance images with 80 consecutive slices in each image. We present results by comparing our automatic method to manually segmented lung cavities provided by an expert radiologist and with those of previous works, showing encouraging results and high robustness of our approach.

Keywords: Active contours, breast cancer, fuzzy c-means segmentation, treatment planning.

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Authors: Mokgadi Moletsane-Kekae, Robert Kananga Mukuna

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The study focused on the analysis of the Adjusted Rorschach Comprehensive System’s responses. The objective of this study is to analyse the participants’ response rate of the Adjusted Rorschach Comprehensive System with regards to critical psychology approach. The use of critical psychology theory in this study was crucial because it responds to the current inadequate western theory or practice in the field of psychology. The study adopted a qualitative approach and a case study design. The study was grounded on interpretivist paradigm. The sample size comprised six learners (three boys and three girls, aged of 14 years) from historically disadvantaged school in the Western Cape, South Africa. The Adjusted Rorschach Comprehensive System (ARCS) administration procedure, biographical information, semi-structured interviews, and observation were used to collect data. Data was analysed using thematic framework. The study found out that, factors that increased the response rates during the administration of ARCS were, language, seating arrangement, drawing, viewing, and describing. The study recommended that, psychological test designers take into consideration the philosophy or worldviews of the local people for whom the test is designed to minimize low response rates.

Keywords: Adjusted Rorschach comprehensive system, critical psychology, learners, responses.

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Authors: G. T. Balakayeva, K. K. Nurlybayeva, M. B. Zhanuzakov

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In the proposed work, we have developed software and designed a software architecture for the implementation of enterprise business processes. The proposed software has a multi-level architecture using a domain-specific tool. The developed architecture is a guarantor of the availability, reliability and security of the system and the implementation of business processes, which are the basis for effective enterprise management. Automating business processes, automating the algorithmic stages of an enterprise, developing optimal algorithms for managing activities, controlling and monitoring, reducing risks and improving results help organizations achieve strategic goals quickly and efficiently. The software described in this article can connect to the corporate information system via two methods: a desktop client and a web client. With an appeal to the application server, the desktop client program connects to the information system on the company's work PCs over a local network. Outside the organization, the user can interact with the information system via a web browser, which acts as a web client and connects to a web server. The developed software consists of several integrated modules that share resources and interact with each other through an API. The following technology stack was used during development: Node js, React js, MongoDB, Ngnix, Cloud Technologies, Python.

Keywords: Algorithms, document processing, automation, integrated modules, software architecture, software design, information system.

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Authors: Yong W. Foo, Lai M. Tang

Abstract:

Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper presents an approach to empower students to actively explore the inner workings of deep neural networks by integrating the student-centered learning approach of flipped classroom models with the investigative capabilities of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Integrating these two factors is crucial for fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: Deep Learning, Explainable AI, AI Visualization, Representation Learning.

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Authors: L. P. K. Liyanage, N. R. P. Withana, A. L. Sandika

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The purpose of this study was to determine tourist and community perception-based sustainable tourism indicators as well as Human Pressure Index (HPI) and Tourist Activity Index (TAI). Study was carried out in Sinharaja forest which is considered as one of the major eco-tourism destination in Sri Lanka. Data were gathered using a pre-tested semi-structured questionnaire as well as records from Forest department. Convenient sampling technique was applied. For the majority of issues, the responses were obtained on multi-point Likert-type scales. Visual portrayal was used for display analyzed data. The study revealed that the host community of the Kudawa gets many benefits from tourism. Also, tourism has caused negative impacts upon the environment and community. The study further revealed the need of proper waste management and involvement of local cultural events for the tourism business in the Kudawa conservation center. The TAI, which accounted to be 1.27 and monthly evolution of HPI revealed that congestion can be occurred in the Sinharaja rainforest during peak season. The results provide useful information to any party involved with tourism planning anywhere, since such attempts would be more effective once the people’s perceptions on these aspects are taken into account.

Keywords: Kudawa conservation center, Sinharaja world heritage rainforest, sustainability indicators.

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Authors: Haiping Ding, Rongchu Zhu, Zhenxia Song

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Local irregular topography has a great impact on earthquake ground motion. For scarp topography, using numerical simulation method, the influence extent and scope of the scarp terrain on scarp's upside and downside ground motion are discussed in case of different vertical incident SV waves. The results show that: (1) The amplification factor of scarp's upside region is greater than that of the free surface, while the amplification factor of scarp's downside part is less than that of the free surface; (2) When the slope angle increases, for x component, amplification factors of the scarp upside also increase, while the downside part decrease with it. For z component, both of the upside and downside amplification factors will increase; (3) When the slope angle changes, the influence scope of scarp's downside part is almost unchanged, but for the upside part, it slightly becomes greater with the increase of slope angle; (4) Due to the existence of the scarp, the z component ground motion appears at the surface. Its amplification factor increases for larger slope angle, and the peaks of the surface responses are related with incident waves. However, the input wave has little effects on the x component amplification factors.

Keywords: Scarp topography, ground motion, amplification factor, vertical incident wave.

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Authors: Vahid R. Asghari, Mehrdad Ardebilipour

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In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter-s spreading sequence. In our previous paper, we used Genetic algorithm (GA), to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. To solve this problem we introduce Particle Swarm Optimization (PSO) into code estimation in spread spectrum communication system. In searching process for code estimation, the PSO algorithm has the merits of rapid convergence to the global optimum, without being trapped in local suboptimum, and good robustness to noise. In this paper we describe how to implement PSO as a component of a searching algorithm in code estimation. Swarm intelligence boasts a number of advantages due to the use of mobile agents. Some of them are: Scalability, Fault tolerance, Adaptation, Speed, Modularity, Autonomy, and Parallelism. These properties make swarm intelligence very attractive for spread spectrum code estimation. They also make swarm intelligence suitable for a variety of other kinds of channels. Our results compare between swarm-based algorithms and Genetic algorithms, and also show PSO algorithm performance in code estimation process.

Keywords: Code estimation, Particle Swarm Optimization(PSO), Spread spectrum.

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Authors: Valeria Tatano, Francesca Guidolin, Francesca Peltrera

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The preservation of historical Italian heritage, at the urban and architectural scale, has to consider restrictions and requirements connected with conservation issues and usability needs, which are often at odds with historical heritage preservation. Recent decades have been marked by the search for increased accessibility not only of public and private buildings, but to the whole historical city, also for people with disability. Moreover, in the last years the concepts of Smart City and Healthy City seek to improve accessibility both in terms of mobility (independent or assisted) and fruition of goods and services, also for historical cities. The principles of Inclusive Design have introduced new criteria for the improvement of public urban space, between current regulations and best practices. Moreover, they have contributed to transforming “special needs” into an opportunity of social innovation. These considerations find a field of research and analysis in the historical city of Venice, which is at the same time a site of UNESCO world heritage, a mass tourism destination bringing in visitors from all over the world and a city inhabited by an aging population. Due to its conformation, Venetian urban fabric is only partially accessible: about four thousand bridges divide thousands of islands, making it almost impossible to move independently. These urban characteristics and difficulties were the base, in the last 20 years, for several researches, experimentations and solutions with the aim of eliminating architectural barriers, in particular for the usability of bridges. The Venetian Municipality with the EBA Office and some external consultants realized several devices (e.g. the “stepped ramp” and the new accessible ramps for the Venice Marathon) that should determine an innovation for the city, passing from the use of mechanical replicable devices to specific architectural projects in order to guarantee autonomy in use. This paper intends to present the state-of-the-art in bridges accessibility, through an analysis based on Inclusive Design principles and on the current national and regional regulation. The purpose is to evaluate some possible strategies that could improve performances, between limits and possibilities of interventions. The aim of the research is to lay the foundations for the development of a strategic program for the City of Venice that could successfully bring together both conservation and improvement requirements.

Keywords: Accessibility and inclusive design, historical heritage preservation, technological and social innovation.

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Authors: Paola Gattinoni, Laura Scesi, Elena Cerino Adbin, Daniele Cremonesi

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The interaction of tunneling or mining with groundwater has become a very relevant problem not only due to the need to guarantee the safety of workers and to assure the efficiency of the tunnel drainage systems, but also to safeguard water resources from impoverishment and pollution risk. Therefore it is very important to forecast the drainage processes (i.e., the evaluation of drained discharge and drawdown caused by the excavation). The aim of this study was to know better the system and to quantify the flow drained from the Fontane mines, located in Val Germanasca (Turin, Italy). This allowed to understand the hydrogeological local changes in time. The work has therefore been structured as follows: the reconstruction of the conceptual model with the geological, hydrogeological and geological-structural study; the calculation of the tunnel inflows (through the use of structural methods) and the comparison with the measured flow rates; the water balance at the basin scale. In this way it was possible to understand what are the relationships between rainfall, groundwater level variations and the effect of the presence of tunnels as a means of draining water. Subsequently, it the effects produced by the excavation of the mining tunnels was quantified, through numerical modeling. In particular, the modeling made it possible to observe the drawdown variation as a function of number, excavation depth and different mines linings.

Keywords: Groundwater, Italy, numerical model, tunneling.

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Authors: Chad A. Long

Abstract:

The United States military is now commonly responding to complex humanitarian emergencies and natural disasters around the world. From catastrophic earthquakes in Haiti to typhoons devastating the Philippines, U.S. military assistance is requested when the event exceeds the local government's ability to assist the population. This study assesses the characteristics of catastrophes that surpass a nation’s individual ability to respond and recover from the event. The paper begins with a historical summary of military aid and then analyzes over 40 years of the United States military humanitarian response. Over 300 military operations were reviewed and coded based on the nature of the disaster. This in-depth study reviewed the U.S. military’s deployment events for cyclones and earthquakes to determine the strength of the natural disaster requiring external assistance. The climatological data for cyclone landfall and magnitude data for earthquake epicenters were identified, grouped into regions and analyzed for time-based trends. The results showed that foreign countries will likely request the U.S. military for cyclones with speeds greater or equal to 125 miles an hour and earthquakes at the magnitude of 7.4 or higher. These results of this study will assist the geographic combatant commands in determining future military response requirements.

Keywords: Cyclones, earthquakes, natural disasters, military.

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Authors: Wenhao Lan, Ning Li, Qiang Tong

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To improve the registration accuracy of a source point cloud and template point cloud when the initial relative deflection angle is too large, a PointNetLK algorithm combined with an oriented bounding box (PointNetLK-OBB) is proposed. In this algorithm, the OBB of a 3D point cloud is used to represent the macro feature of source and template point clouds. Under the guidance of the iterative closest point algorithm, the OBB of the source and template point clouds is aligned, and a mirror symmetry effect is produced between them. According to the fitting degree of the source and template point clouds, the mirror symmetry plane is detected, and the optimal rotation and translation of the source point cloud is obtained to complete the 3D point cloud registration task. To verify the effectiveness of the proposed algorithm, a comparative experiment was performed using the publicly available ModelNet40 dataset. The experimental results demonstrate that, compared with PointNetLK, PointNetLK-OBB improves the registration accuracy of the source and template point clouds when the initial relative deflection angle is too large, and the sensitivity of the initial relative position between the source point cloud and template point cloud is reduced. The primary contribution of this paper is the use of PointNetLK to avoid the non-convex problem of traditional point cloud registration and leveraging the regularity of the OBB to avoid the local optimization problem in the PointNetLK context.

Keywords: Mirror symmetry, oriented bounding box, point cloud registration, PointNetLK-OBB.

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Dhurgham Al-Karawi, Nisreen Polus, Shakir Al-Zaidi, Sabah Jassim

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The management of COVID-19 patients based on chest imaging is emerging as an essential tool for evaluating the spread of the pandemic which has gripped the global community. It has already been used to monitor the situation of COVID-19 patients who have issues in respiratory status. There has been increase to use chest imaging for medical triage of patients who are showing moderate-severe clinical COVID-19 features, this is due to the fast dispersal of the pandemic to all continents and communities. This article demonstrates the development of machine learning techniques for the test of COVID-19 patients using Chest X-Ray (CXR) images in nearly real-time, to distinguish the COVID-19 infection with a significantly high level of accuracy. The testing performance has covered a combination of different datasets of CXR images of positive COVID-19 patients, patients with viral and bacterial infections, also, people with a clear chest. The proposed AI scheme successfully distinguishes CXR scans of COVID-19 infected patients from CXR scans of viral and bacterial based pneumonia as well as normal cases with an average accuracy of 94.43%, sensitivity 95%, and specificity 93.86%. Predicted decisions would be supported by visual evidence to help clinicians speed up the initial assessment process of new suspected cases, especially in a resource-constrained environment.

Keywords: COVID-19, chest x-ray scan, artificial intelligence, texture analysis, local binary pattern transform, Gabor filter.

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in the form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studied with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Wei Zhang, Yao-Yu Li, Yi-Fan Zhu, Qun Li, Wei-Ping Wang

Abstract:

It-s known that incorporating prior knowledge into support vector regression (SVR) can help to improve the approximation performance. Most of researches are concerned with the incorporation of knowledge in form of numerical relationships. Little work, however, has been done to incorporate the prior knowledge on the structural relationships among the variables (referred as to Structural Prior Knowledge, SPK). This paper explores the incorporation of SPK in SVR by constructing appropriate admissible support vector kernel (SV kernel) based on the properties of reproducing kernel (R.K). Three-levels specifications of SPK are studies with the corresponding sub-levels of prior knowledge that can be considered for the method. These include Hierarchical SPK (HSPK), Interactional SPK (ISPK) consisting of independence, global and local interaction, Functional SPK (FSPK) composed of exterior-FSPK and interior-FSPK. A convenient tool for describing the SPK, namely Description Matrix of SPK is introduced. Subsequently, a new SVR, namely Motivated Support Vector Regression (MSVR) whose structure is motivated in part by SPK, is proposed. Synthetic examples show that it is possible to incorporate a wide variety of SPK and helpful to improve the approximation performance in complex cases. The benefits of MSVR are finally shown on a real-life military application, Air-toground battle simulation, which shows great potential for MSVR to the complex military applications.

Keywords: admissible support vector kernel, reproducing kernel, structural prior knowledge, motivated support vector regression

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Authors: Mahdi Hamzehei

Abstract:

In this research, heat transfer of a poly Ethylene fluidized bed reactor without reaction were studied experimentally and computationally at different superficial gas velocities. A multifluid Eulerian computational model incorporating the kinetic theory for solid particles was developed and used to simulate the heat conducting gas–solid flows in a fluidized bed configuration. Momentum exchange coefficients were evaluated using the Syamlal– O-Brien drag functions. Temperature distributions of different phases in the reactor were also computed. Good agreement was found between the model predictions and the experimentally obtained data for the bed expansion ratio as well as the qualitative gas–solid flow patterns. The simulation and experimental results showed that the gas temperature decreases as it moves upward in the reactor, while the solid particle temperature increases. Pressure drop and temperature distribution predicted by the simulations were in good agreement with the experimental measurements at superficial gas velocities higher than the minimum fluidization velocity. Also, the predicted time-average local voidage profiles were in reasonable agreement with the experimental results. The study showed that the computational model was capable of predicting the heat transfer and the hydrodynamic behavior of gas-solid fluidized bed flows with reasonable accuracy.

Keywords: Gas-solid flows, fluidized bed, Hydrodynamics, Heat transfer, Turbulence model, CFD

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Authors: Dennis Corbett

Abstract:

The city of Melbourne in Victoria, Australia, provides a number of examples of how a growing city can integrate urban planning and water planning to achieve sustainable urban development, environmental protection, liveability and integrated water management outcomes, and move towards becoming a “Water Sensitive City". Three examples are provided - the development at Botanic Ridge, where a 318 hectare residential development is being planned and where integrated water management options are being implemented using a “triple bottom line" sustainability investment approach; the Toolern development, which will capture and reuse stormwater and recycled water to greatly reduce the suburb-s demand for potable water, and the development at Kalkallo where a 1,200 hectare industrial precinct development is planned which will merge design of the development's water supply, sewerage services and stormwater system. The Paper argues that an integrated urban planning and water planning approach is fundamental to creating liveable, vibrant communities which meet social and financial needs while being in harmony with the local environment. Further work is required on developing investment frameworks and risk analysis frameworks to ensure that all possible solutions can be assessed equally.

Keywords: Integrated water management, stormwater management, sustainable urban development.

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Authors: Y. Deng, X. Li, C. Kwan, B. Raj, R. Stern

Abstract:

The current speech interfaces in many military applications may be adequate for native speakers. However, the recognition rate drops quite a lot for non-native speakers (people with foreign accents). This is mainly because the nonnative speakers have large temporal and intra-phoneme variations when they pronounce the same words. This problem is also complicated by the presence of large environmental noise such as tank noise, helicopter noise, etc. In this paper, we proposed a novel continuous acoustic feature adaptation algorithm for on-line accent and environmental adaptation. Implemented by incremental singular value decomposition (SVD), the algorithm captures local acoustic variation and runs in real-time. This feature-based adaptation method is then integrated with conventional model-based maximum likelihood linear regression (MLLR) algorithm. Extensive experiments have been performed on the NATO non-native speech corpus with baseline acoustic model trained on native American English. The proposed feature-based adaptation algorithm improved the average recognition accuracy by 15%, while the MLLR model based adaptation achieved 11% improvement. The corresponding word error rate (WER) reduction was 25.8% and 2.73%, as compared to that without adaptation. The combined adaptation achieved overall recognition accuracy improvement of 29.5%, and WER reduction of 31.8%, as compared to that without adaptation.

Keywords: speaker adaptation; environment adaptation; robust speech recognition; SVD; non-native speech recognition

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Authors: Umar Ubandawaki

Abstract:

Political participation involves voluntary and deliberate efforts by the members of a political system to determine the kinds of political institution and individuals that will govern them and equally influence the mobilization and allocation of the available societal resources. Over the years, youths in Nigeria participate actively in political party rallies and voting to elect their leaders and representatives in governance. This paper examines categories and nature of participation in politics as well as factors that drive youths into politics in Sokoto State. A survey conducted, through focus group discussions, interviews and questionnaire, in the six sampled Local Government of Sokoto State identifies three category of political participation; namely, active, moderate and apathetic participation. The findings reveal that 63.57% of respondents are apathetic to politics in the State and unemployed youth constitutes 34.74% of the entire responses. The paper establishes that lack of attainment of need (63.22%) is one of the reasons that make youths engage into participatory activities that encourage political thuggery and manipulation of electoral outcomes. The paper recommends that youths should be engaged into positive rational participatory activities that ensure inclusiveness and promotion of good governance in Nigeria. It is hoped that this will enlighten youths and policy implementers on the constructive strategies in controlling youths’ negative participation in politics in Nigeria.

Keywords: Democracy, Governance, Inclusiveness, Participation and Politics.

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Authors: Guillermo Romero-Jiménez, Luis A. Jiménez-Fraustro, Mayolo Salinas-Camacho, Heriberto Avalos-Valenzuela

Abstract:

A geothermal power plant multiple simulator for operators training is presented. The simulator is designed to be installed in a wireless local area network and has a capacity to train one to six operators simultaneously, each one with an independent simulation session. The sessions must be supervised only by one instructor. The main parts of this multiple simulator are: instructor and operator-s stations. On the instructor station, the instructor controls the simulation sessions, establishes training exercises and supervises each power plant operator in individual way. This station is hosted in a Main Personal Computer (NS) and its main functions are: to set initial conditions, snapshots, malfunctions or faults, monitoring trends, and process and soft-panel diagrams. On the other hand the operators carry out their actions over the power plant simulated on the operator-s stations; each one is also hosted in a PC. The main software of instructor and operator-s stations are executed on the same NS and displayed in PCs through graphical Interactive Process Diagrams (IDP). The geothermal multiple simulator has been installed in the Geothermal Simulation Training Center (GSTC) of the Comisi├│n Federal de Electricidad, (Federal Commission of Electricity, CFE), Mexico, and is being utilized as a part of the training courses for geothermal power plant operators.

Keywords: Geothermal power plant, multiple simulator, training operator.

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Authors: N. Boshkova, K. Kamburova, N. Tabakova, N. Boshkov, Ts. Radeva

Abstract:

Coatings based on polyaniline (PANI) can improve the resistance of steel against corrosion. In this work, the preparation of stable suspensions of colloidal PANI-SiO₂ particles, suitable for obtaining of composite anticorrosive coating on steel, is described. Electrokinetic data as a function of pH are presented, showing that the zeta potentials of the PANI-SiO₂ particles are governed primarily by the charged groups at the silica oxide surface. Electrosteric stabilization of the PANI-SiO₂ particles’ suspension against aggregation is realized at pH>5.5 (EB form of PANI) by adsorption of positively charged polyelectrolyte molecules onto negatively charged PANI-SiO₂ particles. The PANI-SiO₂ particles are incorporated by electrodeposition into the metal matrix of zinc in order to obtain composite (hybrid) coatings. The latter are aimed to ensure sacrificial protection of steel mainly in aggressive media leading to local corrosion damages. The surface morphology of the composite zinc coatings is investigated with SEM. The influence of PANI-SiO₂ particles on the cathodic and anodic processes occurring in the starting electrolyte for obtaining of the coatings is followed with cyclic voltammetry. The electrochemical and corrosion behavior is evaluated with potentiodynamic polarization curves and polarization resistance measurements. The beneficial effect of the stabilized PANI-SiO₂ particles for the increased protective ability of the composites is commented and discussed.

Keywords: Corrosion, polyaniline particles, zinc, protective ability.

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Authors: K. Ishii, T. Furuichi, A. Fujiyama, S. Hariya

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Rice straw pellets are a promising fuel as a renewable energy source. Financial analysis is needed to make a utilization system using rise straw pellets financially feasible, considering all regional conditions including stakeholders related to the collection and storage, production, transportation and heat utilization. We conducted the financial analysis of feasibility for a heat utilization system using rice straw pellets which has been developed for the first time in Nanporo, Hokkaido, Japan. Especially, we attempted to clarify the effect of factors required for the system to be financial feasibility, such as the heating energy demand and collection and storage method of rice straw. The financial feasibility was found to improve when increasing the heating energy demand and collecting wheat straw in August separately from collection of rice straw in November because the costs of storing rice straw and producing pellets were reduced. However, the system remained financially unfeasible. This study proposed a contractor program funded by a subsidy from Nanporo local government where a contracted company, instead of farmers, collects and transports rice straw in order to ensure the financial feasibility of the system, contributing to job creation in the region.

Keywords: Rice straw, pellets, heating energy demand, collection, storage.

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Authors: Zhanna Mingaleva, Irina Mirskikh

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Innovational development of regions in Russia is generally faced with the essential influence from federal and local authorities. The organization of effective mechanism of innovation development (and self-development) is impossible without establishment of defined institutional conditions in the analyzed field. Creative utilization of scientific concepts and information should merge, giving rise to continuing innovation and advanced production. The paper presents an analysis of institutional conditions in the field of creation and development of innovation activity infrastructure and transferring of knowledge and skills between different economic agents in Russia. Knowledge is mainly privately owned, developed through R&D investments and incorporated into technology or a product. Innovation infrastructure is a strong concentration mechanism of advanced facilities, which are mainly located inside large agglomerations or city-regions in order to benefit from scale effects in both input markets (human capital, private financial capital) and output markets (higher education services, research services). The empirical results of the paper show that in the presence of more efficient innovation and knowledge transfer and transcoding system and of a more open attitude of economic agents towards innovation, the innovation and knowledge capacity of regional economy is much higher.

Keywords: knowledge economy, innovational development, transfer of knowledge, institutional preconditions, innovation andknowledge capacity.

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Authors: Agung Budi Muljono, I Made Ginarsa, I Made Ari Nrartha

Abstract:

A large-scale power system (LSPS) consists of two or more sub-systems connected by inter-connecting transmission. Loading pattern on an LSPS always changes from time to time and varies depend on consumer need. The serious instability problem is appeared in an LSPS due to load fluctuation in all of the bus. Adaptive neuro-fuzzy inference system (ANFIS)-based power system stabilizer (PSS) is presented to cover the stability problem and to enhance the stability of an LSPS. The ANFIS control is presented because the ANFIS control is more effective than Mamdani fuzzy control in the computation aspect. Simulation results show that the presented PSS is able to maintain the stability by decreasing peak overshoot to the value of −2.56 × 10−5 pu for rotor speed deviation Δω2−3. The presented PSS also makes the settling time to achieve at 3.78 s on local mode oscillation. Furthermore, the presented PSS is able to improve the peak overshoot and settling time of Δω3−9 to the value of −0.868 × 10−5 pu and at the time of 3.50 s for inter-area oscillation.

Keywords: ANFIS, large-scale, power system, PSS, stability enhancement.

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Authors: Mohammed Sharif, Donald Burn

Abstract:

Large scale climate signals and their teleconnections can influence hydro-meteorological variables on a local scale. Several extreme flow and timing measures, including high flow and low flow measures, from 62 hydrometric stations in Canada are investigated to detect possible linkages with several large scale climate indices. The streamflow data used in this study are derived from the Canadian Reference Hydrometric Basin Network and are characterized by relatively pristine and stable land-use conditions with a minimum of 40 years of record. A composite analysis approach was used to identify linkages between extreme flow and timing measures and climate indices. The approach involves determining the 10 highest and 10 lowest values of various climate indices from the data record. Extreme flow and timing measures for each station were examined for the years associated with the 10 largest values and the years associated with the 10 smallest values. In each case, a re-sampling approach was applied to determine if the 10 values of extreme flow measures differed significantly from the series mean. Results indicate that several stations are impacted by the large scale climate indices considered in this study. The results allow the determination of any relationship between stations that exhibit a statistically significant trend and stations for which the extreme measures exhibit a linkage with the climate indices.

Keywords: flood analysis, low-flow events, climate change, trend analysis, Canada

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Authors: Yao-Hong Tsai

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Due to the sensor technology, video surveillance has become the main way for security control in every big city in the world. Surveillance is usually used by governments for intelligence gathering, the prevention of crime, the protection of a process, person, group or object, or the investigation of crime. Many surveillance systems based on computer vision technology have been developed in recent years. Moving target tracking is the most common task for Unmanned Aerial Vehicle (UAV) to find and track objects of interest in mobile aerial surveillance for civilian applications. The paper is focused on vision-based collision avoidance for UAVs by recurrent neural networks. First, images from cameras on UAV were fused based on deep convolutional neural network. Then, a recurrent neural network was constructed to obtain high-level image features for object tracking and extracting low-level image features for noise reducing. The system distributed the calculation of the whole system to local and cloud platform to efficiently perform object detection, tracking and collision avoidance based on multiple UAVs. The experiments on several challenging datasets showed that the proposed algorithm outperforms the state-of-the-art methods.

Keywords: Unmanned aerial vehicle, object tracking, deep learning, collision avoidance.

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Authors: Hariton N. Costin, Iulian Ciocoiu, Tudor Barbu, Cristian Rotariu

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In this paper three different approaches for person verification and identification, i.e. by means of fingerprints, face and voice recognition, are studied. Face recognition uses parts-based representation methods and a manifold learning approach. The assessment criterion is recognition accuracy. The techniques under investigation are: a) Local Non-negative Matrix Factorization (LNMF); b) Independent Components Analysis (ICA); c) NMF with sparse constraints (NMFsc); d) Locality Preserving Projections (Laplacianfaces). Fingerprint detection was approached by classical minutiae (small graphical patterns) matching through image segmentation by using a structural approach and a neural network as decision block. As to voice / speaker recognition, melodic cepstral and delta delta mel cepstral analysis were used as main methods, in order to construct a supervised speaker-dependent voice recognition system. The final decision (e.g. “accept-reject" for a verification task) is taken by using a majority voting technique applied to the three biometrics. The preliminary results, obtained for medium databases of fingerprints, faces and voice recordings, indicate the feasibility of our study and an overall recognition precision (about 92%) permitting the utilization of our system for a future complex biometric card.

Keywords: Biometry, image processing, pattern recognition, speech analysis.

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Authors: P. Chambe, B. Canova, A. Balabanian, M. Pele, N. Coeur

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To avoid battery assisted tags with limited lifetime batteries, it is proposed here to replace them by energy harvesting systems, able to feed from local environment. This would allow total independence to RFID systems, very interesting for applications where tag removal from its location is not possible. Example is here described for luggage safety in airports, and is easily  extendable to similar situation in terms of operation constraints. The idea is to fix RFID tag with energy harvesting system not only to identify luggage but also to supply an embedded microcontroller with a sensor delivering luggage weight making it impossible to add or to remove anything from the luggage during transit phases. The aim is to optimize the harvested energy for such RFID applications, and to study in which limits these applications are theoretically possible. Proposed energy harvester is based on two energy sources: piezoelectricity and electromagnetic waves, so that when the luggage is moving on ground transportation to airline counters, the piezo module supplies the tag and its microcontroller, while the RF module operates during luggage transit thanks to readers located along the way. Tag location on the luggage is analyzed to get best vibrations, as well as harvester better choice for optimizing the energy supply depending on applications and the amount of energy harvested during a period of time. Effects of system parameters (RFID UHF frequencies, limit distance between the tag and the antenna necessary to harvest energy, produced voltage and voltage threshold) are discussed and working conditions for such system are delimited.

Keywords: EM waves, Energy Harvesting, Piezoelectric, RFID Tag.

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Authors: Christos Bouras, Eirini Stergiou, Charitini Karakostaki, Vasileios Tzanos, Vasileios Kokkinos

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Wind power represents a major pathway to curtailing greenhouse gas emissions and thus reducing the rate of climate change. A wind turbine runs practically emission-free for 20 years, representing one of the most environmentally sustainable sources of energy. Nevertheless, environmental and biodiversity concerns can often slow down or halt the deployment of wind farms due to local public opposition. This opposition is often fuelled by poor relationships between wind energy stakeholders and civil society, which in many cases led to conflictual protests and property damage. In this context, addressing these concerns is essential in order to facilitate the proliferation of wind farms in Europe and the phase-out of fossil fuels from the energy mix. The aim of this study is to identify a number of good practices and cases to avoid increasing biodiversity protection at all stages of wind farms’ lifecycle in three participating countries, namely Greece, Latvia, and Poland. The results indicate that although available technological solutions are already being exploited worldwide, in these countries, there is still room for improvement. To address this gap, a set of policy recommendations is proposed to accomplish the wind energy targets in the near future while simultaneously mitigating the pertinent biodiversity risks.

Keywords: Biodiversity protection, environmental impact, social acceptance, wind energy.

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Authors: Naoto Suzuki

Abstract:

Medical methods to completely treat glaucoma are yet to be developed. Therefore, ophthalmologists manage patients mainly to delay disease progression. Patients with glaucoma are mainly elderly individuals. In elderly people's houses, having an equipment that can provide medical treatment and care can release their family from their care. For elderly people with the glaucoma to live by themselves as much as possible, we developed a support robot having five functions: elderly people care, ophthalmological examination, trip assistance to the neighborhood, medical treatment, and data referral to a hospital. The medical and nursing care robot should approach the visual field that the patients can see at a speed suitable for their eyesight. This is because the robot will be dangerous if it approaches the patients from the visual field that they cannot see. We experimentally developed a robot that brings a white cane to elderly people with glaucoma. The base part of the robot is a carriage, which is a Megarover 1.1, and it has two infrared sensors. The robot moves along a white line on the floor using the infrared sensors and has a special arm, which does not use electricity. The arm can scoop the block attached to the white cane. Next, we also developed a direction detector comprised of a charge-coupled device camera (SVR41ResucueHD; Sun Mechatronics), goggles (MG-277MLF; Midori Anzen Co. Ltd.), and biconvex lenses with a focal length of 25 mm (Edmund Co.). Some young people were photographed using the direction detector, which was put on their faces. Image processing was performed using Scilab 6.1.0 and Image Processing and Computer Vision Toolbox 4.1.2. To measure the people's line of vision, we calculated the iris's center of gravity using five processes: reduction, trimming, binarization or gray scale, edge extraction, and Hough transform. We compared the binarization and gray scale processes in image processing. The binarization process was better than the gray scale process. For edge extraction, we compared five methods: Sobel, Prewitt, Laplacian of Gaussian, fast Fourier transform, and Canny. The Canny method was the optimal extraction method. We performed the Hough transform to search for the main coordinates from the iris's edge, and we found that the Hough transform could calculate the center point of the iris.

Keywords: Glaucoma, support robot, elderly people, Hough transform, direction detector, line of vision.

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